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Large language models are excellent at understanding intent and generating language — but they don't natively know what the temperature in London is right now. The solution is tool use (also called function calling): you define a structured tool specification, register it with the LLM, and when the model needs live data it emits a special tool_use message instead of a text response. Your backend intercepts that signal, calls the real data source, and feeds the result back int

We design, deploy, and operate end-to-end AI systems for Indian B2B and B2C businesses, powered by Sarvam AI. As the Indian government pushes for broader digital inclusion, a massive challenge remains: the linguistic divide. With over 22 official languages and thousands of dialects, traditional text-based portals and English-first IVR systems are failing the "Next Half Billion" internet users. To bridge this gap, GovTech integrators and enterprise IT firms are shifting to Voi

A complete technical guide to building an AI PDF Chatbot using LangChain, FAISS, and OpenAI. Covers RAG architecture, chunking strategy, vector store setup, system prompt design, and deployment — without the source code guesswork.

Stuck on backpropagation, RAG pipelines, or a transformer implementation? Codersarts connects you with AI experts who deliver clean, explained code — fast. Every type of help, every phase of the curriculum.

Vector Database Job Support & Interview Preparation — Get the Role You Are Targeting Vector database skills are now tested in interviews at Google, Amazon, Flipkart, Swiggy, PhonePe, Meesho, CRED, and every AI-first startup. But most preparation resources stop at theory — they do not prepare you for the system design rounds, coding challenges, and production-depth questions that top companies actually ask. At Codersarts, our experts prepare you with the exact content that get

Add AI Search to Your Existing App — Without Rebuilding Everything Your users expect a search that actually understands what they are looking for. Adding semantic AI search to an existing product does not require a complete rebuild — it requires the right integration strategy. We add vector search to live applications without disrupting your current codebase, database, or user experience. How We Integrate AI Search into Your App ✓ Audit your existing data and search requirem

Vector Database Implementation Help — Expert Developers, Production-Ready Code Implementing a vector database is not like adding a REST API endpoint. It requires the right embedding model, indexing strategy, query architecture, and integration layer — and getting any one of them wrong costs weeks of debugging and re-work. At Codersarts, our vector database engineers have built production pipelines across every major platform — Pinecone, Weaviate, Qdrant, Milvus, ChromaDB, pgv

RAG Project Help for Students — Build AI That Reads Your Documents Retrieval-Augmented Generation (RAG) is the most in-demand AI project topic of 2025. Almost every university AI, NLP, and data science course now includes a RAG-based project — and it is one of the most technically challenging to implement from scratch. Our experts help you design, build, and understand a complete RAG pipeline so you can demonstrate it confidently in your viva, presentation, or project report.

Vector Database: Powering Semantic Search, AI Retrieval, and Scalable Intelligence Vector Database Assignment Help — Expert Support for Students Struggling with your vector database assignment? You are not alone. Vector databases are one of the fastest-growing and most challenging topics in modern AI and machine learning courses — and most students have very little hands-on support when it comes to implementation. At Codersarts, our vector database experts provide clear, step

Building a strong portfolio is essential when you want to showcase your skills in data science. It helps you stand out to employers, clients, or collaborators. But creating a portfolio can feel overwhelming if you don’t know where to start or how to organize your work. I’m here to guide you through the process step-by-step. By the end, you will have a clear plan to build your portfolio data projects efficiently and effectively. Why You Need a Portfolio Data Projects A portfol

Assignment Overview Scenario: You are a research engineer at an academic institution building tools to help researchers manage and analyze scientific literature. Your task is to create an advanced MCP server that not only provides access to research papers but also uses AI sampling (server-initiated LLM calls) to generate intelligent summaries, extract key findings, and compare papers. This assignment builds on Assignment 1 by adding Module 4 concepts: sampling, production pa

Assignment Overview Scenario: You are a software engineer at a productivity software company. Your team is developing an AI-powered personal assistant that helps users manage their daily tasks, projects, and goals. Your task is to build an MCP server that provides intelligent task management capabilities to Claude Desktop, allowing users to interact with their task lists using natural language. Learning Objectives: Implement MCP tools for CRUD operations on task data Design a

Purpose In Assignment 1, you built the core protocol and task-handling pieces. In this assignment, you will extend that foundation into a more realistic system that supports streaming responses, multi-turn conversation continuity, framework integration, observability, and production-minded orchestration. This assignment draws heavily on Chapters 6–7 and asks you to show how FastA2A behaves when it is used as the coordination layer for real application workflows. Connection to

Introduction During a disaster, incident commanders must process conflicting data from dozens of sources, coordinate resources across multiple agencies, and make life-safety decisions under extreme time pressure. Traditional tools and static decision trees cannot keep pace with rapidly evolving, multi-agency incidents. AI-Powered Emergency Response Agents built on Retrieval-Augmented Generation (RAG) address this by continuously retrieving real-time situational data, historic

Purpose This assignment requires you to build a working multi-agent service using the FastA2A design principles covered in Chapters 1–5. You will implement agent identity, capability metadata, structured messages, tasks, context handling, routing, and a basic request/response workflow that mirrors the FastA2A protocol model. Your solution should demonstrate how a real agent system moves from discovery to execution and then back to the client with a stable, inspectable task li

Introduction Programming industrial robots requires mastery of multiple proprietary controller languages, motion planning algorithms, safety standards, and constantly evolving vendor documentation. This knowledge burden slows deployments, escalates costs, and creates dangerous skill gaps across automation teams. Robot Programming Assistance Systems powered by Retrieval-Augmented Generation (RAG) address this by dynamically retrieving relevant vendor documentation, safety stan

Duration: 10–14 days following Assignment 1 Type: Individual Assignment Difficulty Level: Advanced Marks: 100 This assignment requires you to create an original, domain-specific cost optimization strategy for a real-world LLM application that you research or design. Unlike Assignment 1 (general framework), Assignment 2 demands independent research, creative problem-solving, and domain expertise. Learning Objectives Research real LLM cost challenges in a specific industry. Des

Course: LLM Cost Engineering — From Token Economics to Production Monitoring Student Level: Undergraduate Year 3 / Postgraduate Submission Platform: Moodle (Learning Management System) Individual / Group: Individual Assignments Purpose This assignment requires you to design and implement a comprehensive cost optimization framework for a real or hypothetical LLM-powered application. You will leverage every core concept introduced across Chapters 1–9: understand token economics

Course: Agent Discovery & Agent Cards Level: Medium to Advanced Type: Individual Duration: 7 to 10 days Objective This assignment tests your ability to build the Client Agent side of agent discovery, design and operate a shared Agent Registry, and harden an agent discovery system against trust and security failures. By completing this assignment, you will have implemented the complete five-step Client Agent discovery workflow, built both exact and LLM-assisted skill matching,

Course: Agent Discovery & Agent Cards Level: Beginner to Medium Type: Individual Duration: 5 to 7 days Objective This assignment tests your understanding of why agent discovery exists, what every field in an Agent Card communicates, and how to publish a valid, discoverable Agent Card from an A2A server. By completing this assignment, you will have written Agent Cards from scratch, identified and fixed common authoring mistakes, served cards via both FastAPI and FastA2A, and b